/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    MetaCost.java
 *    Copyright (C) 2002 University of Waikato
 *
 */

package weka.classifiers.meta;

import weka.classifiers.*;
import weka.classifiers.rules.ZeroR;
import java.io.*;
import java.util.*;
import weka.core.*;
import weka.filters.Filter;


/**
 * This metaclassifier makes its base classifier cost-sensitive using the
 * method specified in <p>
 *
 * Pedro Domingos (1999). <i>MetaCost: A general method for making classifiers
 * cost-sensitive</i>, Proceedings of the Fifth International Conference on 
 * Knowledge Discovery and Data Mining, pp. 155-164. Also available online at
 * <a href="http://www.cs.washington.edu/homes/pedrod/kdd99.ps.gz">
 * http://www.cs.washington.edu/homes/pedrod/kdd99.ps.gz</a>. <p>
 *
 * This classifier should produce similar results to one created by
 * passing the base learner to Bagging, which is in turn passed to a
 * CostSensitiveClassifier operating on minimum expected cost. The difference
 * is that MetaCost produces a single cost-sensitive classifier of the
 * base learner, giving the benefits of fast classification and interpretable
 * output (if the base learner itself is interpretable). This implementation 
 * uses all bagging iterations when reclassifying training data (the MetaCost
 * paper reports a marginal improvement when only those iterations containing
 * each training instance are used in reclassifying that instance). <p>
 *
 * Valid options are:<p>
 *
 * -W classname <br>
 * Specify the full class name of a classifier (required).<p>
 *
 * -C cost file <br>
 * File name of a cost matrix to use. If this is not supplied, a cost
 * matrix will be loaded on demand. The name of the on-demand file
 * is the relation name of the training data plus ".cost", and the
 * path to the on-demand file is specified with the -N option.<p>
 *
 * -N directory <br>
 * Name of a directory to search for cost files when loading costs on demand
 * (default current directory). <p>
 *
 * -I num <br>
 * Set the number of bagging iterations (default 10). <p>
 *
 * -S seed <br>
 * Random number seed used when reweighting by resampling (default 1).<p>
 *
 * -P num <br>
 * Size of each bag, as a percentage of the training size (default 100). <p>
 *
 * -cost-matrix matrix<br>
 * The cost matrix, specified in Matlab single line format.<p>
 *
 * Options after -- are passed to the designated classifier.<p>
 *
 * @author Len Trigg (len@reeltwo.com)
 * @version $Revision: 1.15.2.1 $ 
 */
public class MetaCost extends RandomizableSingleClassifierEnhancer {

  /* Specify possible sources of the cost matrix */
  public static final int MATRIX_ON_DEMAND = 1;
  public static final int MATRIX_SUPPLIED = 2;
  public static final Tag [] TAGS_MATRIX_SOURCE = {
    new Tag(MATRIX_ON_DEMAND, "Load cost matrix on demand"),
    new Tag(MATRIX_SUPPLIED, "Use explicit cost matrix")
  };

  /** Indicates the current cost matrix source */
  protected int m_MatrixSource = MATRIX_ON_DEMAND;

  /** 
   * The directory used when loading cost files on demand, null indicates
   * current directory 
   */
  protected File m_OnDemandDirectory = new File(System.getProperty("user.dir"));

  /** The name of the cost file, for command line options */
  protected String m_CostFile;

  /** The cost matrix */
  protected CostMatrix m_CostMatrix = new CostMatrix(1);

  /** The number of iterations. */
  protected int m_NumIterations = 10;

  /** The size of each bag sample, as a percentage of the training size */
  protected int m_BagSizePercent = 100;
    
  /**
   * Returns a string describing classifier
   * @return a description suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
 
    return  "This metaclassifier makes its base classifier cost-sensitive using the "
      + "method specified in\n\n"
      + "Pedro Domingos (1999) \"MetaCost: A general method for making classifiers "
      + "cost-sensitive\", Proceedings of the Fifth International Conference on "
      + "Knowledge Discovery and Data Mining, pp 155-164.\n\n"
      + "This classifier should produce similar results to one created by "
      + "passing the base learner to Bagging, which is in turn passed to a "
      + "CostSensitiveClassifier operating on minimum expected cost. The difference "
      + "is that MetaCost produces a single cost-sensitive classifier of the "
      + "base learner, giving the benefits of fast classification and interpretable "
      + "output (if the base learner itself is interpretable). This implementation  "
      + "uses all bagging iterations when reclassifying training data (the MetaCost "
      + "paper reports a marginal improvement when only those iterations containing "
      + "each training instance are used in reclassifying that instance).";
 
  }

  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {

    Vector newVector = new Vector(6);

    newVector.addElement(new Option(
	      "\tNumber of bagging iterations.\n"
	      + "\t(default 10)",
	      "I", 1, "-I <num>"));
    newVector.addElement(new Option(
	      "\tFile name of a cost matrix to use. If this is not supplied,\n"
              +"\ta cost matrix will be loaded on demand. The name of the\n"
              +"\ton-demand file is the relation name of the training data\n"
              +"\tplus \".cost\", and the path to the on-demand file is\n"
              +"\tspecified with the -N option.",
	      "C", 1, "-C <cost file name>"));
    newVector.addElement(new Option(
              "\tName of a directory to search for cost files when loading\n"
              +"\tcosts on demand (default current directory).",
              "N", 1, "-N <directory>"));
    newVector.addElement(new Option(
              "\tThe cost matrix in Matlab single line format.",
              "cost-matrix", 1, "-cost-matrix <matrix>"));
    newVector.addElement(new Option(
              "\tSize of each bag, as a percentage of the\n" 
              + "\ttraining set size. (default 100)",
              "P", 1, "-P"));

    Enumeration enu = super.listOptions();
    while (enu.hasMoreElements()) {
      newVector.addElement(enu.nextElement());
    }
    return newVector.elements();
  }

  /**
   * Parses a given list of options. Valid options are:<p>
   *
   * -W classname <br>
   * Specify the full class name of a classifier (required).<p>
   *
   * -C cost file <br>
   * File name of a cost matrix to use. If this is not supplied, a cost
   * matrix will be loaded on demand. The name of the on-demand file
   * is the relation name of the training data plus ".cost", and the
   * path to the on-demand file is specified with the -N option.<p>
   *
   * -N directory <br>
   * Name of a directory to search for cost files when loading costs on demand
   * (default current directory). <p>
   *
   * -I num <br>
   * Set the number of bagging iterations (default 10). <p>
   *
   * -S seed <br>
   * Random number seed used when reweighting by resampling (default 1).<p>
   *
   * -P num <br>
   * Size of each bag, as a percentage of the training size (default 100). <p>
   *
   * -cost-matrix matrix<br>
   * The cost matrix, specified in Matlab single line format.<p>
   *
   * Options after -- are passed to the designated classifier.<p>
   *
   * @param options the list of options as an array of strings
   * @exception Exception if an option is not supported
   */
  public void setOptions(String[] options) throws Exception {

    String bagIterations = Utils.getOption('I', options);
    if (bagIterations.length() != 0) {
      setNumIterations(Integer.parseInt(bagIterations));
    } else {
      setNumIterations(10);
    }

    String bagSize = Utils.getOption('P', options);
    if (bagSize.length() != 0) {
      setBagSizePercent(Integer.parseInt(bagSize));
    } else {
      setBagSizePercent(100);
    }

    String costFile = Utils.getOption('C', options);
    if (costFile.length() != 0) {
      setCostMatrix(new CostMatrix(new BufferedReader(
                                   new FileReader(costFile))));
      setCostMatrixSource(new SelectedTag(MATRIX_SUPPLIED,
                                          TAGS_MATRIX_SOURCE));
      m_CostFile = costFile;
    } else {
      setCostMatrixSource(new SelectedTag(MATRIX_ON_DEMAND, 
                                          TAGS_MATRIX_SOURCE));
    }
    
    String demandDir = Utils.getOption('N', options);
    if (demandDir.length() != 0) {
      setOnDemandDirectory(new File(demandDir));
    }

    String cost_matrix = Utils.getOption("cost-matrix", options);
    if (cost_matrix.length() != 0) {
      StringWriter writer = new StringWriter();
      CostMatrix.parseMatlab(cost_matrix).write(writer);
      setCostMatrix(new CostMatrix(new StringReader(writer.toString())));
      setCostMatrixSource(new SelectedTag(MATRIX_SUPPLIED,
                                          TAGS_MATRIX_SOURCE));
    }
    
    super.setOptions(options);
  }

  /**
   * Gets the current settings of the Classifier.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String [] getOptions() {


    String [] superOptions = super.getOptions();
    String [] options;

    options = new String [superOptions.length + 6];
    int current = 0;

    if (m_MatrixSource == MATRIX_SUPPLIED) {
      if (m_CostFile != null) {
        options[current++] = "-C";
        options[current++] = "" + m_CostFile;
      }
      else {
        options[current++] = "-cost-matrix";
        options[current++] = getCostMatrix().toMatlab();
      }
    } else {
      options[current++] = "-N";
      options[current++] = "" + getOnDemandDirectory();
    }
    options[current++] = "-I"; options[current++] = "" + getNumIterations();
    options[current++] = "-P"; options[current++] = "" + getBagSizePercent();

    System.arraycopy(superOptions, 0, options, current, 
		     superOptions.length);
    return options;
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String costMatrixSourceTipText() {
    return "Gets the source location method of the cost matrix. Will "
      + "be one of MATRIX_ON_DEMAND or MATRIX_SUPPLIED.";
  }

  /**
   * Gets the source location method of the cost matrix. Will be one of
   * MATRIX_ON_DEMAND or MATRIX_SUPPLIED.
   *
   * @return the cost matrix source.
   */
  public SelectedTag getCostMatrixSource() {

    return new SelectedTag(m_MatrixSource, TAGS_MATRIX_SOURCE);
  }
  
  /**
   * Sets the source location of the cost matrix. Values other than
   * MATRIX_ON_DEMAND or MATRIX_SUPPLIED will be ignored.
   *
   * @param newMethod the cost matrix location method.
   */
  public void setCostMatrixSource(SelectedTag newMethod) {
    
    if (newMethod.getTags() == TAGS_MATRIX_SOURCE) {
      m_MatrixSource = newMethod.getSelectedTag().getID();
    }
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String onDemandDirectoryTipText() {
    return "Name of directory to search for cost files when loading "
      + "costs on demand.";
  }

  /**
   * Returns the directory that will be searched for cost files when
   * loading on demand.
   *
   * @return The cost file search directory.
   */
  public File getOnDemandDirectory() {

    return m_OnDemandDirectory;
  }

  /**
   * Sets the directory that will be searched for cost files when
   * loading on demand.
   *
   * @param newDir The cost file search directory.
   */
  public void setOnDemandDirectory(File newDir) {

    if (newDir.isDirectory()) {
      m_OnDemandDirectory = newDir;
    } else {
      m_OnDemandDirectory = new File(newDir.getParent());
    }
    m_MatrixSource = MATRIX_ON_DEMAND;
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String bagSizePercentTipText() {
    return "The size of each bag, as a percentage of the training set "
      + "size.";
  }

  /**
   * Gets the size of each bag, as a percentage of the training set size.
   *
   * @return the bag size, as a percentage.
   */
  public int getBagSizePercent() {

    return m_BagSizePercent;
  }
  
  /**
   * Sets the size of each bag, as a percentage of the training set size.
   *
   * @param newBagSizePercent the bag size, as a percentage.
   */
  public void setBagSizePercent(int newBagSizePercent) {

    m_BagSizePercent = newBagSizePercent;
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String numIterationsTipText() {
    return "The number of bagging iterations.";
  }
  
  /**
   * Sets the number of bagging iterations
   */
  public void setNumIterations(int numIterations) {

    m_NumIterations = numIterations;
  }

  /**
   * Gets the number of bagging iterations
   *
   * @return the maximum number of bagging iterations
   */
  public int getNumIterations() {
    
    return m_NumIterations;
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String costMatrixTipText() {
    return "A misclassification cost matrix.";
  }

  /**
   * Gets the misclassification cost matrix.
   *
   * @return the cost matrix
   */
  public CostMatrix getCostMatrix() {
    
    return m_CostMatrix;
  }
  
  /**
   * Sets the misclassification cost matrix.
   *
   * @param the cost matrix
   */
  public void setCostMatrix(CostMatrix newCostMatrix) {
    
    m_CostMatrix = newCostMatrix;
    m_MatrixSource = MATRIX_SUPPLIED;
  }

  /**
   * Builds the model of the base learner.
   *
   * @param data the training data
   * @exception Exception if the classifier could not be built successfully
   */
  public void buildClassifier(Instances data) throws Exception {

    if (!data.classAttribute().isNominal()) {
      throw new UnsupportedClassTypeException("Class attribute must be nominal!");
    }
    if (m_MatrixSource == MATRIX_ON_DEMAND) {
      String costName = data.relationName() + CostMatrix.FILE_EXTENSION;
      File costFile = new File(getOnDemandDirectory(), costName);
      if (!costFile.exists()) {
        throw new Exception("On-demand cost file doesn't exist: " + costFile);
      }
      setCostMatrix(new CostMatrix(new BufferedReader(
                                   new FileReader(costFile))));
    }

    // Set up the bagger
    Bagging bagger = new Bagging();
    bagger.setClassifier(getClassifier());
    bagger.setSeed(getSeed());
    bagger.setNumIterations(getNumIterations());
    bagger.setBagSizePercent(getBagSizePercent());
    bagger.buildClassifier(data);
    
    // Use the bagger to reassign class values according to minimum expected
    // cost
    Instances newData = new Instances(data);
    for (int i = 0; i < newData.numInstances(); i++) {
      Instance current = newData.instance(i);
      double [] pred = bagger.distributionForInstance(current);
      int minCostPred = Utils.minIndex(m_CostMatrix.expectedCosts(pred));
      current.setClassValue(minCostPred);
    }

    // Build a classifier using the reassigned data
    m_Classifier.buildClassifier(newData);
  }

  /**
   * Classifies a given test instance.
   *
   * @param instance the instance to be classified
   * @exception Exception if instance could not be classified
   * successfully
   */
  public double classifyInstance(Instance instance) throws Exception {

    return m_Classifier.classifyInstance(instance);
  }

  /**
   * Gets the classifier specification string, which contains the
   * class name of the classifier and any options to the classifier
   *
   * @return the classifier string.  */
  protected String getClassifierSpec() {
    
    Classifier c = getClassifier();
    return c.getClass().getName() + " "
      + Utils.joinOptions(((OptionHandler)c).getOptions());
  }

  /**
   * Output a representation of this classifier
   */
  public String toString() {

    if (m_Classifier == null) {
      return "MetaCost: No model built yet.";
    }

    String result = "MetaCost cost sensitive classifier induction";
    result += "\nOptions: " + Utils.joinOptions(getOptions());
    result += "\nBase learner: " + getClassifierSpec()
      + "\n\nClassifier Model\n"
      + m_Classifier.toString()
      + "\n\nCost Matrix\n"
      + m_CostMatrix.toString();
    
    return result;
  }

  /**
   * Main method for testing this class.
   *
   * @param argv should contain the following arguments:
   * -t training file [-T test file] [-c class index]
   */
  public static void main(String [] argv) {

    try {
      System.out.println(Evaluation
			 .evaluateModel(new MetaCost(),
					argv));
    } catch (Exception e) {
      e.printStackTrace();
      System.err.println(e.getMessage());
    }
  }

}
